Ionotropic Glutamate receptors are membrane proteins that are responsible for initiation of excitatory transmission between most neural cells. Activation of these important neurotransmitter receptors is involved in a number of neurodegenerative diseases, including stroke and epilepsy. In addition, drugs that enhance the activity of glutamate at the AMPA subtype of glutamate receptors (allosteric modulators) have been shown to improve cognition and may have benefits in neurodegenerative diseases such as Alzheimer's disease. NMDA type glutamate receptors are important for long term potentiating, which is important to learning and memory. Due to their importance therefore much work has been put in recent decade into research to characterize glutamate receptors structural organization and functional mechanisms. The first full receptor structure of the AMPA type GluA2 receptor has recently been published. Yet, we are far from complete understanding of how these receptors function, especially at the quantitative level. This proposal seeks to develop hypothetical yet quantitative models of the free energy differences of different conformational states of the ligand binding domain dimers and the transmembrane domain that will aid further refinement of functional model, our understanding of mechanism of this channel functioning and in aiding further experimental and theoretical work by producing testable hypotheses and models or further refinement at the higher resolution. We will use methods of computational chemistry: molecular dynamics simulations and continuum electrostatics to compute free energies of distinct conformational states of the ligand binding and transmembrane domains and compare, how differences in relative free energies change for different protein sub-types, mutants and in the presence or absence of the ligands. This will allow us to decipher the functional mechanisms of the receptor at the quantitative level and eventually achieve detailed molecular models of such proteins that will have predictive power and will become useful in, e.g. rational drug design.